Overview

Brought to you by YData

Dataset statistics

Number of variables10
Number of observations499
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory29.5 KiB
Average record size in memory60.5 B

Variable types

Numeric7
Categorical3

Alerts

Ordenes_Correctivo is highly overall correlated with Total_OrdenesHigh correlation
Ordenes_Preventivo is highly overall correlated with Total_OrdenesHigh correlation
Total_Ordenes is highly overall correlated with Ordenes_Correctivo and 1 other fieldsHigh correlation
ID_Equipo is uniformly distributed Uniform
ID_Equipo has unique values Unique

Reproduction

Analysis started2025-02-21 09:23:05.532989
Analysis finished2025-02-21 09:23:17.963453
Duration12.43 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

ID_Equipo
Real number (ℝ)

Uniform  Unique 

Distinct499
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean250
Minimum1
Maximum499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-02-21T09:23:18.103523image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile25.9
Q1125.5
median250
Q3374.5
95-th percentile474.1
Maximum499
Range498
Interquartile range (IQR)249

Descriptive statistics

Standard deviation144.19316
Coefficient of variation (CV)0.57677263
Kurtosis-1.2
Mean250
Median Absolute Deviation (MAD)125
Skewness0
Sum124750
Variance20791.667
MonotonicityStrictly increasing
2025-02-21T09:23:18.371433image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
329 1
 
0.2%
342 1
 
0.2%
341 1
 
0.2%
340 1
 
0.2%
339 1
 
0.2%
338 1
 
0.2%
337 1
 
0.2%
336 1
 
0.2%
335 1
 
0.2%
Other values (489) 489
98.0%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
499 1
0.2%
498 1
0.2%
497 1
0.2%
496 1
0.2%
495 1
0.2%
494 1
0.2%
493 1
0.2%
492 1
0.2%
491 1
0.2%
490 1
0.2%

Tipo_Equipo
Categorical

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size831.0 B
Generador
135 
Transformador
125 
Compresor
121 
Motor
118 

Length

Max length13
Median length9
Mean length9.0561122
Min length5

Characters and Unicode

Total characters4519
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMotor
2nd rowMotor
3rd rowMotor
4th rowTransformador
5th rowCompresor

Common Values

ValueCountFrequency (%)
Generador 135
27.1%
Transformador 125
25.1%
Compresor 121
24.2%
Motor 118
23.6%

Length

2025-02-21T09:23:18.761848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-21T09:23:19.072333image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
generador 135
27.1%
transformador 125
25.1%
compresor 121
24.2%
motor 118
23.6%

Most occurring characters

ValueCountFrequency (%)
r 1005
22.2%
o 863
19.1%
e 391
 
8.7%
a 385
 
8.5%
n 260
 
5.8%
d 260
 
5.8%
s 246
 
5.4%
m 246
 
5.4%
G 135
 
3.0%
T 125
 
2.8%
Other values (5) 603
13.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4519
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 1005
22.2%
o 863
19.1%
e 391
 
8.7%
a 385
 
8.5%
n 260
 
5.8%
d 260
 
5.8%
s 246
 
5.4%
m 246
 
5.4%
G 135
 
3.0%
T 125
 
2.8%
Other values (5) 603
13.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4519
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 1005
22.2%
o 863
19.1%
e 391
 
8.7%
a 385
 
8.5%
n 260
 
5.8%
d 260
 
5.8%
s 246
 
5.4%
m 246
 
5.4%
G 135
 
3.0%
T 125
 
2.8%
Other values (5) 603
13.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4519
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 1005
22.2%
o 863
19.1%
e 391
 
8.7%
a 385
 
8.5%
n 260
 
5.8%
d 260
 
5.8%
s 246
 
5.4%
m 246
 
5.4%
G 135
 
3.0%
T 125
 
2.8%
Other values (5) 603
13.3%

Fabricante
Categorical

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size831.0 B
ABB
131 
Siemens
129 
GE
123 
Schneider
116 

Length

Max length9
Median length7
Mean length5.1823647
Min length2

Characters and Unicode

Total characters2586
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSchneider
2nd rowABB
3rd rowGE
4th rowSiemens
5th rowABB

Common Values

ValueCountFrequency (%)
ABB 131
26.3%
Siemens 129
25.9%
GE 123
24.6%
Schneider 116
23.2%

Length

2025-02-21T09:23:19.283754image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-21T09:23:19.551618image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
abb 131
26.3%
siemens 129
25.9%
ge 123
24.6%
schneider 116
23.2%

Most occurring characters

ValueCountFrequency (%)
e 490
18.9%
B 262
10.1%
S 245
9.5%
i 245
9.5%
n 245
9.5%
A 131
 
5.1%
m 129
 
5.0%
s 129
 
5.0%
G 123
 
4.8%
E 123
 
4.8%
Other values (4) 464
17.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2586
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 490
18.9%
B 262
10.1%
S 245
9.5%
i 245
9.5%
n 245
9.5%
A 131
 
5.1%
m 129
 
5.0%
s 129
 
5.0%
G 123
 
4.8%
E 123
 
4.8%
Other values (4) 464
17.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2586
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 490
18.9%
B 262
10.1%
S 245
9.5%
i 245
9.5%
n 245
9.5%
A 131
 
5.1%
m 129
 
5.0%
s 129
 
5.0%
G 123
 
4.8%
E 123
 
4.8%
Other values (4) 464
17.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2586
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 490
18.9%
B 262
10.1%
S 245
9.5%
i 245
9.5%
n 245
9.5%
A 131
 
5.1%
m 129
 
5.0%
s 129
 
5.0%
G 123
 
4.8%
E 123
 
4.8%
Other values (4) 464
17.9%

Modelo
Categorical

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size831.0 B
Z300
141 
Y200
126 
X100
121 
M400
111 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1996
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowX100
2nd rowZ300
3rd rowY200
4th rowX100
5th rowY200

Common Values

ValueCountFrequency (%)
Z300 141
28.3%
Y200 126
25.3%
X100 121
24.2%
M400 111
22.2%

Length

2025-02-21T09:23:19.782398image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-21T09:23:19.998250image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
z300 141
28.3%
y200 126
25.3%
x100 121
24.2%
m400 111
22.2%

Most occurring characters

ValueCountFrequency (%)
0 998
50.0%
Z 141
 
7.1%
3 141
 
7.1%
Y 126
 
6.3%
2 126
 
6.3%
X 121
 
6.1%
1 121
 
6.1%
M 111
 
5.6%
4 111
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1996
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 998
50.0%
Z 141
 
7.1%
3 141
 
7.1%
Y 126
 
6.3%
2 126
 
6.3%
X 121
 
6.1%
1 121
 
6.1%
M 111
 
5.6%
4 111
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1996
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 998
50.0%
Z 141
 
7.1%
3 141
 
7.1%
Y 126
 
6.3%
2 126
 
6.3%
X 121
 
6.1%
1 121
 
6.1%
M 111
 
5.6%
4 111
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1996
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 998
50.0%
Z 141
 
7.1%
3 141
 
7.1%
Y 126
 
6.3%
2 126
 
6.3%
X 121
 
6.1%
1 121
 
6.1%
M 111
 
5.6%
4 111
 
5.6%

Potencia_kW
Real number (ℝ)

Distinct471
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2420.6553
Minimum-100
Maximum4997
Zeros0
Zeros (%)0.0%
Negative10
Negative (%)2.0%
Memory size4.0 KiB
2025-02-21T09:23:20.212025image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile249.8
Q11233
median2406
Q33563.5
95-th percentile4786.1
Maximum4997
Range5097
Interquartile range (IQR)2330.5

Descriptive statistics

Standard deviation1443.2171
Coefficient of variation (CV)0.59620927
Kurtosis-1.1039458
Mean2420.6553
Median Absolute Deviation (MAD)1168
Skewness0.054996689
Sum1207907
Variance2082875.7
MonotonicityNot monotonic
2025-02-21T09:23:20.504084image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-100 10
 
2.0%
562 3
 
0.6%
799 2
 
0.4%
1536 2
 
0.4%
670 2
 
0.4%
4948 2
 
0.4%
3212 2
 
0.4%
404 2
 
0.4%
1514 2
 
0.4%
4080 2
 
0.4%
Other values (461) 470
94.2%
ValueCountFrequency (%)
-100 10
2.0%
53 1
 
0.2%
79 1
 
0.2%
137 1
 
0.2%
146 1
 
0.2%
153 1
 
0.2%
163 1
 
0.2%
173 1
 
0.2%
187 1
 
0.2%
197 1
 
0.2%
ValueCountFrequency (%)
4997 1
0.2%
4993 1
0.2%
4992 1
0.2%
4974 1
0.2%
4948 2
0.4%
4947 1
0.2%
4945 1
0.2%
4930 1
0.2%
4929 1
0.2%
4914 1
0.2%
Distinct489
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5225.1603
Minimum525
Maximum9993
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-02-21T09:23:20.791282image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum525
5-th percentile855.5
Q12896
median5258
Q37451.5
95-th percentile9478.4
Maximum9993
Range9468
Interquartile range (IQR)4555.5

Descriptive statistics

Standard deviation2744.958
Coefficient of variation (CV)0.5253347
Kurtosis-1.1560925
Mean5225.1603
Median Absolute Deviation (MAD)2259
Skewness-0.029294541
Sum2607355
Variance7534794.5
MonotonicityNot monotonic
2025-02-21T09:23:21.106650image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
655 2
 
0.4%
1783 2
 
0.4%
2270 2
 
0.4%
8101 2
 
0.4%
2386 2
 
0.4%
9026 2
 
0.4%
1717 2
 
0.4%
6992 2
 
0.4%
5938 2
 
0.4%
1534 2
 
0.4%
Other values (479) 479
96.0%
ValueCountFrequency (%)
525 1
0.2%
549 1
0.2%
564 1
0.2%
572 1
0.2%
585 1
0.2%
618 1
0.2%
654 1
0.2%
655 2
0.4%
661 1
0.2%
663 1
0.2%
ValueCountFrequency (%)
9993 1
0.2%
9933 1
0.2%
9926 1
0.2%
9921 1
0.2%
9916 1
0.2%
9909 1
0.2%
9881 1
0.2%
9851 1
0.2%
9836 1
0.2%
9829 1
0.2%

Total_Ordenes
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.93988
Minimum7
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-02-21T09:23:21.331364image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile13
Q117
median20
Q323
95-th percentile28
Maximum36
Range29
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.5654975
Coefficient of variation (CV)0.22896314
Kurtosis0.068828658
Mean19.93988
Median Absolute Deviation (MAD)3
Skewness0.31398371
Sum9950
Variance20.843768
MonotonicityNot monotonic
2025-02-21T09:23:21.534952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
18 47
 
9.4%
19 43
 
8.6%
21 39
 
7.8%
20 37
 
7.4%
17 37
 
7.4%
16 36
 
7.2%
24 36
 
7.2%
23 35
 
7.0%
15 34
 
6.8%
22 29
 
5.8%
Other values (19) 126
25.3%
ValueCountFrequency (%)
7 1
 
0.2%
8 1
 
0.2%
9 1
 
0.2%
10 1
 
0.2%
11 5
 
1.0%
12 10
 
2.0%
13 12
 
2.4%
14 19
3.8%
15 34
6.8%
16 36
7.2%
ValueCountFrequency (%)
36 1
 
0.2%
34 1
 
0.2%
33 1
 
0.2%
32 2
 
0.4%
31 3
 
0.6%
30 6
 
1.2%
29 6
 
1.2%
28 7
1.4%
27 12
2.4%
26 16
3.2%

Ordenes_Correctivo
Real number (ℝ)

High correlation 

Distinct20
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.138277
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-02-21T09:23:21.796813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q18
median10
Q312
95-th percentile15.1
Maximum21
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.2608974
Coefficient of variation (CV)0.32164218
Kurtosis-0.0018880075
Mean10.138277
Median Absolute Deviation (MAD)2
Skewness0.27670445
Sum5059
Variance10.633452
MonotonicityNot monotonic
2025-02-21T09:23:22.009429image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
10 59
11.8%
9 56
11.2%
11 56
11.2%
8 52
10.4%
12 49
9.8%
7 44
8.8%
13 41
8.2%
14 34
6.8%
6 28
5.6%
5 22
 
4.4%
Other values (10) 58
11.6%
ValueCountFrequency (%)
1 1
 
0.2%
3 1
 
0.2%
4 14
 
2.8%
5 22
 
4.4%
6 28
5.6%
7 44
8.8%
8 52
10.4%
9 56
11.2%
10 59
11.8%
11 56
11.2%
ValueCountFrequency (%)
21 1
 
0.2%
20 2
 
0.4%
19 3
 
0.6%
18 3
 
0.6%
17 7
 
1.4%
16 9
 
1.8%
15 17
 
3.4%
14 34
6.8%
13 41
8.2%
12 49
9.8%

Ordenes_Preventivo
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8016032
Minimum2
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-02-21T09:23:22.238166image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q18
median10
Q312
95-th percentile15
Maximum23
Range21
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.1949253
Coefficient of variation (CV)0.32595946
Kurtosis0.3073908
Mean9.8016032
Median Absolute Deviation (MAD)2
Skewness0.26313678
Sum4891
Variance10.207548
MonotonicityNot monotonic
2025-02-21T09:23:22.425666image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
10 72
14.4%
11 66
13.2%
9 54
10.8%
12 51
10.2%
8 50
10.0%
6 37
7.4%
7 36
7.2%
13 30
6.0%
5 26
 
5.2%
14 22
 
4.4%
Other values (9) 55
11.0%
ValueCountFrequency (%)
2 3
 
0.6%
3 4
 
0.8%
4 13
 
2.6%
5 26
 
5.2%
6 37
7.4%
7 36
7.2%
8 50
10.0%
9 54
10.8%
10 72
14.4%
11 66
13.2%
ValueCountFrequency (%)
23 1
 
0.2%
19 1
 
0.2%
18 5
 
1.0%
17 6
 
1.2%
16 11
 
2.2%
15 11
 
2.2%
14 22
 
4.4%
13 30
6.0%
12 51
10.2%
11 66
13.2%

Vida_util_estimada
Real number (ℝ)

Distinct487
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94637.06
Minimum70524
Maximum99982
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-02-21T09:23:22.647568image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum70524
5-th percentile85025.1
Q192276
median96218
Q398421
95-th percentile99751.9
Maximum99982
Range29458
Interquartile range (IQR)6145

Descriptive statistics

Standard deviation5040.6204
Coefficient of variation (CV)0.053262648
Kurtosis3.5753992
Mean94637.06
Median Absolute Deviation (MAD)2777
Skewness-1.6436326
Sum47223893
Variance25407854
MonotonicityNot monotonic
2025-02-21T09:23:22.897767image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99333 2
 
0.4%
94216 2
 
0.4%
89711 2
 
0.4%
99935 2
 
0.4%
86426 2
 
0.4%
93986 2
 
0.4%
99079 2
 
0.4%
98035 2
 
0.4%
99861 2
 
0.4%
94122 2
 
0.4%
Other values (477) 479
96.0%
ValueCountFrequency (%)
70524 1
0.2%
70889 1
0.2%
71579 1
0.2%
72510 1
0.2%
75876 1
0.2%
77730 1
0.2%
78763 1
0.2%
79409 1
0.2%
79443 1
0.2%
79794 1
0.2%
ValueCountFrequency (%)
99982 1
0.2%
99939 1
0.2%
99935 2
0.4%
99933 1
0.2%
99905 1
0.2%
99898 1
0.2%
99890 1
0.2%
99881 1
0.2%
99874 1
0.2%
99872 1
0.2%

Interactions

2025-02-21T09:23:15.479896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:05.903513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:07.585792image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:09.237156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:10.739239image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:12.225747image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:13.801090image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:15.691000image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:06.123053image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:07.806800image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:09.448667image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:10.955418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:12.465910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:13.998160image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:15.902485image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:06.324371image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:08.040977image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:09.663491image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:11.176211image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:12.681403image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:14.462880image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:16.118674image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:06.534253image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:08.340391image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:09.862541image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:11.381948image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:12.893392image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:14.653245image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:16.330313image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:06.821992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:08.570524image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:10.073207image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:11.586870image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:13.130441image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:14.852537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:16.621943image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:07.098745image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:08.822125image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:10.327215image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:11.808853image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:13.360213image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:15.065775image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:16.987821image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:07.330233image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:09.022557image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:10.513918image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:12.005890image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:13.564221image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-21T09:23:15.274641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2025-02-21T09:23:23.128976image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
FabricanteHoras_Recomendadas_RevisionID_EquipoModeloOrdenes_CorrectivoOrdenes_PreventivoPotencia_kWTipo_EquipoTotal_OrdenesVida_util_estimada
Fabricante1.0000.0600.0870.0320.0000.0800.0350.0760.0000.065
Horas_Recomendadas_Revision0.0601.0000.0160.0000.0060.0050.0830.0000.013-0.040
ID_Equipo0.0870.0161.0000.051-0.0120.0130.0040.0000.009-0.065
Modelo0.0320.0000.0511.0000.0000.0000.0000.0000.0180.096
Ordenes_Correctivo0.0000.006-0.0120.0001.0000.001-0.0520.0000.6980.003
Ordenes_Preventivo0.0800.0050.0130.0000.0011.0000.0040.0700.6840.046
Potencia_kW0.0350.0830.0040.000-0.0520.0041.0000.000-0.041-0.056
Tipo_Equipo0.0760.0000.0000.0000.0000.0700.0001.0000.0000.000
Total_Ordenes0.0000.0130.0090.0180.6980.684-0.0410.0001.0000.020
Vida_util_estimada0.065-0.040-0.0650.0960.0030.046-0.0560.0000.0201.000

Missing values

2025-02-21T09:23:17.378178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-21T09:23:17.807332image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ID_EquipoTipo_EquipoFabricanteModeloPotencia_kWHoras_Recomendadas_RevisionTotal_OrdenesOrdenes_CorrectivoOrdenes_PreventivoVida_util_estimada
01MotorSchneiderX1001009965620101092645.0
12MotorABBZ30012202165137691899.0
23MotorGEY200373366741610697734.0
34TransformadorSiemensX10066214802012893640.0
45CompresorABBY20098242821710799898.0
56CompresorGEZ3004426874724131184467.0
67TransformadorABBY20018301238115693947.0
78TransformadorGEY200532384627161193045.0
89CompresorABBX100348496881612479443.0
910CompresorSchneiderM400322753051810898758.0
ID_EquipoTipo_EquipoFabricanteModeloPotencia_kWHoras_Recomendadas_RevisionTotal_OrdenesOrdenes_CorrectivoOrdenes_PreventivoVida_util_estimada
489490TransformadorSiemensX100-10051181441092265.0
490491TransformadorSiemensZ300350752581913695653.0
491492GeneradorSchneiderM40025030832214894216.0
492493TransformadorSiemensZ30028897172189999027.0
493494TransformadorGEZ3001875331158794081.0
494495CompresorSchneiderY2004383618823121198566.0
495496MotorSiemensX1003617559421101190614.0
496497GeneradorSchneiderY200548861422111196443.0
497498MotorSchneiderM4002602587421101191463.0
498499MotorABBM40087769071541198945.0